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Curtsy, a clothing resale app aimed at Gen Z women, raises $11 million Series A

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Curtsy, a clothing resale app and competitor to recently IPO’d Poshmark, announced today it has raised $11 million in Series A funding for its startup focused on the Gen Z market. The app, which evolved out of an earlier effort for renting dresses, now allows women to list their clothes, shoes and accessories for resale, while also reducing many of the frictions involved with the typical resale process.

The new round was led by Index Ventures, and included participation from Y Combinator, prior investors FJ Labs and 1984 Ventures, and angel investor Josh Breinlinger (who left Jackson Square Ventures to start his own fund).

To date, Curtsy has raised $14.5 million, including over two prior rounds, which also included investors CRV, SV Angel, Kevin Durant, Priscilla Scala and other angels.

Like other online clothing resale businesses, Curtsy aims to address the needs of a younger generation of consumers who are looking for a more sustainable alternative when shopping for clothing. Instead of constantly buying new, many Gen Z consumers will rotate their wardrobes over time, often by leveraging resale apps.

Image Credits: Curtsy

However, the current process for listing your own clothes on resale apps can be time-consuming. A recent report by Wired, for example, detailed how many women were spinning their wheels engaging with Poshmark in the hopes of making money from their closets, to little avail. The Poshmark sellers complained they had to do more than just list, sell, package and ship their items — they also had to participate in the community in order to have their items discovered.

Curtsy has an entirely different take. It wants to make it easier and faster for casual sellers to list items by reducing the amount of work involved to sell. It also doesn’t matter how many followers a seller has, which makes its marketplace more welcoming to first-time sellers.

“The big gap in the market is really for casual sellers — people who are not interested in selling professionally,” explains Curtsy CEO David Oates. “In pretty much every other app that you’ve heard about, pro sellers really crowd out everyday women. Part of that is the friction of the whole process,” he says.

On Curtsy, the listing process is far more streamlined.

The app uses a combination of machine learning and human review to help the sellers merchandise their items, which increase their chances of selling. When sellers first list their item in the app, Curtsy will recommend a price, then fill in details like the brand, category, subcategory, shipping weight and the suggested selling price, using machine learning systems training on the previous items sold on its marketplace. Human review fixes any errors in that process.

Also before items are posted, Curtsy improves and crops the images, as well as fixes any other issues with the listing, and moderates listings for spam. This process helps to standardize the listings on the app across all sellers, giving everyone a fair shot at having their items discovered and purchased.

Another unique feature is how Curtsy caters to the Gen Z to young Millennial user base (ages 15-30), who are often without shipping supplies or even a printer for producing a shipping label.

Image Credit: Curtsy / Photo credit: Brooke Ray

First-time sellers receive a free starter kit with Curtsy-branded supplies for packaging their items at home, like poly mailers in multiple sizes. As they need more supplies, the cost of those is built into the selling flow, so you don’t have to explicitly pay for it — it’s just deducted from your earnings. Curtsy also helps sellers to schedule a free USPS pickup to save a trip to the post office, and it will even send sellers a shipping label, if need be.

“One of the things we realized quickly is Gen Z does not really have printers. So we actually have a label service and we’ll send you the label in the mail for free from centers across the country,” says Oates.

Later, when a buyer of an item purchased from Curtsy is ready to resell it, they can do so with one tap — they don’t have to photograph it and describe it again. This also speeds up the selling process.

Overall, the use of technology, outsourced teams who improve listings and extra features like supplies and labels can be expensive. But Curtsy believes the end result is that they can bring more casual sellers to the resale market.

“Whatever costs we have, they should be in service of increased liquidity, so we can grow faster and add more people,” Oates says. “In case of the label service, those are people who otherwise wouldn’t be able to participate in selling online. There’s no other app that would allow them to sell without a printer.”

Image Credits: Curtsy

This system, so far, appears to be working. Curtsy now has several hundred thousand people who buy and sell on its iOS-only app, with an average transaction rates of three items bought or sold per month. When the new round closed late in 2020, the company was reporting a $25 million GMV revenue run rate, and average monthly growth of around 30%. Today, Curtsy generates revenue by taking a 20% commission on sales (or $3 for items under $15).

The team, until recently, was only five people — including co-founders David Oates, William Ault, Clara Agnes Ault and Eli Allen, plus a contract workforce. With the Series A, Curtsy will be expanding, specifically by investing in new roles within product and marketing to help it scale. It will also be focused on developing an Android version of its app in the first quarter of 2021 and further building out its web presence.

“Never before have we seen such a strong overlap between buyers and sellers on a consumer-to-consumer marketplace,” said Damir Becirovic of Index Ventures, about the firm’s investment. “We believe the incredible love for Curtsy is indicative of a large marketplace in the making,” he added.

Lyron Foster is a Hawaii based African American Musician, Author, Actor, Blogger, Filmmaker, Philanthropist and Multinational Serial Tech Entrepreneur.

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MealMe raises $900,000 for its food search engine

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This morning MealMe.ai, a food search engine, announced that it has closed a $900,000 pre-seed round. Palm Drive Capital led the round, with participation from Slow Ventures and CP Ventures.

TechCrunch first became familiar with MealMe when it presented as part of the Techstars Atlanta demo day last October, mentioning it in a roundup of favorite startups from a group of the accelerator’s startup cohorts.

The company’s product allows users to search for food, or a restaurant. It then displays price points from various food-delivery apps for what the user wants to eat and have delivered. And, notably, MealMe allows for in-app checkout, regardless of the selected provider.

The service could boost pricing and delivery-speed transparency amongst the different apps that help folks eat, like DoorDash and Uber Eats. But Mealme didn’t start out looking to build a search engine. Instead it took a few changes in direction to get there.

From social network to search engine

MealMe is an example of a startup whose first idea proved only directionally correct. The company began life as a food-focused social network, co-founder Matthew Bouchner told TechCrunch. That iteration of the service allowed users to view posted food pictures, and then find ordering options for what they saw.

While still operating as a social network, MealMe applied to both Y Combinator and Techstars, but wasn’t accepted at either.

The startup discovered that some of its users were posting food pics simply to get the service to tell them which delivery services would be able to bring them what they wanted. From that learning the company focused on building a food search engine, allowing users to search for restaurants, and then vet various delivery options and prices. That iteration of the product got the company into Techstars Atlanta, eventually leading to the demo day that TechCrunch reviewed.

During its time in Techstars, the company adjusted its model to not merely link to DoorDash and others, but to handle checkout inside of its own application. This captures more gross merchandize value (GMV) inside of MealMe, Bouchner explained in an interview. The capability was rolled out in September of 2020.

Since then the company has seen rapid growth, which it measures at around 20% week-on-week. During TechCrunch’s interview with MealMe, the company said that it had reached a GMV run rate of more than $500,000, and was scaling toward the $1 million mark. In the intervening weeks the company passed the $1 million GMV run-rate threshold.

MealMe was slightly coy on its business model, but it appears to make margin between what it charges users for orders and the total revenue it passes along to food delivery apps.

TechCrunch was curious about platform risk at MealMe; could the company get away with offering price comparison and ordering across multiple third-party delivery services without raising the ire of the companies behind those apps? At the time of our interview, Bouchner said that his company had not seen pushback from the services it sends users to. His company’s goal is to grow quickly, become a useful revenue source for the DoorDashes of the world, and then reach out for some of formal agreement, he explained.

“We continue to be a powerful revenue generator and drive thousands of orders to food delivery services per week,” the co-founder said in a written statement. Certainly MealMe found investors more excited by its growth than concerned about Uber Eats or other apps cutting the startup off from their service.

What first caught my eye about MealMe was the realization of how much I would have used it in my early 20s. Perhaps the company can find enough users like my younger self to help it scale to sufficient size that it can go to the major food ordering companies and demand a cut, not merely avoid being cut off.

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Apple supplier Foxconn reaches tentative agreement to build Fisker’s next electric car

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Apple supplier Foxconn Technology Group has reached a tentative agreement with electric vehicle startup-turned-SPAC Fisker to develop and eventually manufacture an EV that will be sold in North America, Europe, China and India.

Fisker and Foxconn said Wednesday that a memorandum of understanding agreement has been signed. Discussions between the two companies will continue with the expectation that a formal partnership agreement will be reached during the second quarter of this year. 

Under the agreement, Foxconn will begin production in the fourth quarter of 2023 with a projected annual volume of more than 250,000 vehicles. The electric vehicle will carry the Fisker brand.

Foxconn Technology Group Chairman Young-way Liu touted the company’s vertically integrated global supply chain and accumulated engineering capabilities, noting that it gives the company two major advantages in the development and manufacturing of the key elements of an EV, which includes the electric motor, electric control module and battery.

That supply chain and ability to scale engineering quickly will be critical for Foxconn if it hopes to meet its production target.

“The collaboration between our firms means that it will only take 24 months to produce the next Fisker vehicle — from research and development to production, reducing half of the traditional time required to bring a new vehicle to market,” Young-way Liu said in a statement.

Fisker said production of the Ocean SUV — its first EV and one that is supposed to be built by contract manufacturer Magna — will begin in the fourth quarter of 2022. The company said it plans to unveil a production-intent prototype of the Ocean later this year.

This is not Foxconn’s first foray into electric vehicle manufacturing.

Foxconn announced in January 2020 that it had formed a joint venture with Fiat Chrysler Automobiles to build electric vehicles in China. Under that agreement, each party will own 50% of the venture to develop and manufacture electric vehicles and engage in an IOV, what Foxconn parent company Hon Hai calls the “internet of vehicles” business.

Last month, Foxconn and Chinese automaker Zhejiang Geely Holding Group agreed to form a joint venture focused on contract manufacturing for automakers, with a specific focus on electrification, connectivity and autonomous driving technology as well as vehicles designed for sharing.

The joint venture between Foxconn and Geely will provide consulting services on whole vehicles, parts, intelligent drive systems and other automotive ecosystem platforms to automakers as well as ridesharing companies. Geely said it will bring its experience in the automotive fields of design, engineering, R&D, intelligent manufacturing, supply chain management and quality control while Foxconn will bring its manufacturing and Information and Communication Technology (ICT) know-how.

 

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Select Star raises seed to automatically document datasets for data scientists

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Back when I was a wee lad with a very security-compromised MySQL installation, I used to answer every web request with multiple “SELECT *” database requests — give me all the data and I’ll figure out what to do with it myself.

Today in a modern, data-intensive org, “SELECT *” will kill you. With petabytes of information, tens of thousands of tables (on the small side!), and millions and perhaps billions of calls flung at the database server, data science teams can no longer just ask for all the data and start working with it immediately.

Big data has led to the rise of data warehouses and data lakes (and apparently data lake houses), infrastructure to make accessing data more robust and easy. There is still a cataloguing and discovery problem though — just because you have all of your data in one place doesn’t mean a data scientist knows what the data represents, who owns it, or what that data might affect in the myriad of web and corporate reporting apps built on top of it.

That’s where Select Star comes in. The startup, which was founded about a year ago in March 2020, is designed to automatically build out metadata within the context of a data warehouse. From there, it offers a full-text search that allows users to quickly find data as well as “heat map” signals in its search results which can quickly pinpoint which columns of a dataset are most used by applications within a company and have the most queries that reference them.

The product is SaaS, and it is designed to allow for quick onboarding by connecting to a customer’s data warehouse or business intelligence (BI) tool.

Select Star’s interface allows data scientists to understand what data they are looking at. Photo via Select Star.

Shinji Kim, the sole founder and CEO, explained that the tool is a solution to a problem she has seen directly in corporate data science teams. She formerly founded Concord Systems, a real-time data processing startup that was acquired by Akamai in 2016. “The part that I noticed is that we now have all the data and we have the ability to compute, but now the next challenge is to know what the data is and how to use it,” she explained.

She said that “tribal knowledge is starting to become more wasteful [in] time and pain in growing companies” and pointed out that large companies like Facebook, Airbnb, Uber, Lyft, Spotify and others have built out their own homebrewed data discovery tools. Her mission for Select Star is to allow any corporation to quickly tap into an easy-to-use platform to solve this problem.

The company raised a $2.5 million seed round led by Bowery Capital with participation from Background Capital and a number of prominent angels including Spencer Kimball, Scott Belsky, Nick Caldwell, Michael Li, Ryan Denehy and TLC Collective.

Data discovery tools have been around in some form for years, with popular companies like Alation having raised tens of millions of VC dollars over the years. Kim sees an opportunity to compete by offering a better onboarding experience and also automating large parts of the workflow that remain manual for many alternative data discovery tools. With many of these tools, “they don’t do the work of connecting and building the relationship,” between data she said, adding that “documentation is still important, but being able to automatically generate [metadata] allows data teams to get value right away.”

Select Star’s team, with CEO and founder Shinji Kim in top row, middle. Photo via Select Star.

In addition to just understanding data, Select Star can help data engineers begin to figure out how to change their databases without leading to cascading errors. The platform can identify how columns are used and how a change to one may affect other applications or even other datasets.

Select Star is coming out of private beta today. The company’s team currently has seven people, and Kim says they are focused on growing the team and making it even easier to onboard users by the end of the year.

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